Jim Arnold (jimmyjamesarnold)

jimmyjamesarnold

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Location:Boston, MA

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Jim Arnold's repositories

fungiForecast

A repo to model success finding mushroom in a given location at a given time of year.

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Awesome-Bioinformatics

A curated list of awesome Bioinformatics libraries and software.

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mimic-code

MIMIC Code Repository: Code shared by the research community for the MIMIC-III database

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notebooks

folder for stats and modeling practice

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NLTK_notebooks

Notebooks working through nltk chapters

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DataChallenge

For honing DS skills

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10000-dice-game-simulator-and-stats

First project on GitHub - 10000 dice game simulator in python

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miningSocMed

Exercises in mining social media.

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LightGBM

A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.

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annoPipeline

Uses mygene.info and Entrez APIs to annotate gene lists.

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sampleproject

Following tutorial on https://packaging.python.org/tutorials/packaging-projects/

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biopython

Official git repository for Biopython (converted from CVS)

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flavor-data

Data from OpenAg - my analysis of Basil compounds formed under different conditions

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mygene.py

mygene is an easy-to-use Python wrapper to access MyGene.Info services.

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insightProject

Tool for mining insights from Sunshine Act data

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eating-for-two

Insight Project

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Economic-Impact-of-Automation-US

I found a great data set linking US job data with the probability of automation in the near future. on data.world and wanted to explore.

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Intro-to-Microbiome-Analysis-in-R

Exploring microbiome analysis tools in R

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cobrapy

COBRApy is a package for constraint-based modeling of biological networks

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cookiecutter

A command-line utility that creates projects from cookiecutters (project templates). E.g. Python package projects, jQuery plugin projects.

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googlesheets

Google Spreadsheets R API

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sklearn-deap

Use evolutionary algorithms instead of gridsearch in scikit-learn

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conda-envs

My conda environment YAML files

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